Learning with constrained and unlabelled data

• Published in: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) Vol. 1; pp. 731 - 738 vol. 1
• Main Authors: Lange, T., Law, M.H.C., Jain, A.K., Buhmann, J.M.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 2005
• Subjects: Computer science; Computer vision; Constraint optimization; Cost function; Entropy; Face detection; Fitting; Image segmentation; Labeling; Object recognition
• ISBN: 0769523722; 9780769523729
• ISSN: 1063-6919; 1063-6919
• DOI: 10.1109/CVPR.2005.210

Classification problems abundantly arise in many computer vision tasks eing of supervised, semi-supervised or unsupervised nature. Even when class labels are not available, a user still might favor certain grouping solutions over others. This bias can be expressed either by providing a clustering criterion or cost function and, in addition to that, by specifying pairwise constraints on the assignment of objects to classes. In this work, we discuss a unifying formulation for labelled and unlabelled data that can incorporate constrained data for model fitting. Our approach models the constraint information by the maximum entropy principle. This modeling strategy allows us (i) to handle constraint violations and soft constraints, and, at the same time, (ii) to speed up the optimization process. Experimental results on face classification and image segmentation indicates that the proposed algorithm is computationally efficient and generates superior groupings when compared with alternative techniques.